Respiratory Pathogen Dynamics in Community Fever Cases — Jiangsu Province, China (2023-2024)

Respiratory Pathogen Dynamics in Community Fever Cases — Jiangsu Province, China (2023-2024) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Respiratory Pathogen Dynamics in Community Fever Cases — Jiangsu Province, China (2023-2024) Fei Deng, Zhuhan Dong, Tian Qiu, Ke Xu, Qigang Dai, Huiyan Yu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4645900/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Sep, 2024 Read the published version in Virology Journal → Version 1 posted 11 You are reading this latest preprint version Abstract Background Respiratory infectious disease was the world's highest incidence of infectious diseases, it was caused by a variety of respiratory pathogens, and the current monitoring of respiratory pathogens in the world focused on influenza and coronavirus. This study aimed to establish the pathogen spectrum of local acute respiratory infections and to further study the co-infection of pathogens. Time series models commonly used to predict infectious diseases can effectively predict disease outbreaks and serve as auxiliary tools for disease surveillance and response strategy formulation. Methods From June 2023 to February 2024, we collected influenza-like illness (ILI) cases weekly from the community in Xuanwu District, Nanjing, and obtained a total of 2,046 samples. We established a spectrum of respiratory pathogens in Nanjing and analyzed the age distribution and symptom counts associated with various pathogens. We compared age, gender, symptom counts, and viral loads between individuals with co-infections and those with single infections. An autoregressive comprehensive moving average model (ARIMA) was constructed to predict the incidence of respiratory infectious diseases. Results Among 2046 samples, the total detection rate of respiratory pathogen nucleic acids was 53.57% (1096/2046), with influenza A virus 503 cases (24.00%), influenza B virus 224 cases (10.95%), and HCoV 95 cases (4.64%) being predominant. Some pathogens were statistically significant in age and number of symptoms. The positive rate of mixed infections was 6.11% (125/2046), There was no significant difference in age and number of symptoms between co-infection and simple infection. After multiple iterative analyses, an ARIMA model (0,1,4), (0,0,0) was established as the optimal model, with an R 2 value of 0.930, indicating good predictive performance. Conclusions In the past, the spectrum of respiratory pathogens in Nanjing, Jiangsu Province was complex, and the main age groups of different viruses were different, causing different symptoms, and the co-infection of viruses had no correlation with the age and gender of patients. The ARIMA model provided an estimate of future incidence, which plateaued in subsequent months. Acute respiratory tract infection Pathogen spectrum Arima model Virus Bacteria Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Global Burden of Disease (GBD) studies show that respiratory infectious diseases cause approximately 145,000 deaths globally every year [ 1 ] , in 2019, the number of cases of upper respiratory tract infection was as high as 17.2 billion, accounting for 42.83% of all disease cases in the GBD database, and the incidence rate was the highest [ 2 ] , posing a huge threat to global human health. Currently, respiratory tract infections rank second in the global burden of disease among children and adolescents, making it one of the leading causes of rising morbidity and mortality worldwide [ 3 ] . The susceptible population of respiratory infectious diseases is wide, the transmission route is easy to achieve, and often caused by a variety of pathogens, and the infection situation is complex and often overlapping, so the detection work is often very tedious [ 4 ] . Currently, there is more research in China on monitoring and controlling novel coronaviruses and influenza viruses [ 5 – 6 ] , with less focus on monitoring other pathogens. This study conducts multi-pathogen testing on feverish populations in Xuanwu District, Nanjing City, establishes a local respiratory pathogen spectrum, understands the epidemiological patterns of pathogens, and provides more basis for the diagnosis and treatment of respiratory infections in patients with unknown pathogens in clinical practice. From 2019 to 2023, the world experienced a pandemic of the novel coronavirus (COVID-19). As the virulence of COVID-19 weakened, community transmission decreased, and vaccination coverage became more widespread, the severity of COVID-19 appeared to decrease. However, it still poses a threat due to its strong infectivity. COVID-19 prevention and control remain a hot topic in the field of public health [ 7 ] . In recent years, China and other countries have explored the impact of non-pharmaceutical interventions (NPIs) [ 8 – 10 ] related to novel coronavirus on the prevalence of respiratory infectious diseases. However, most studies have not included the post-NPIS period. This study aims to establish the current respiratory pathogen spectrum and compare the impact and changes in respiratory infections after the cessation of NPIs. In recent years, there has been an increasing trend in using mathematical models for infectious disease early warning and prediction [ 11 – 12 ] . However, the direction of respiratory tract infection prediction remains relatively underexplored. Establishing effective and accurate predictive models to forecast the future trends of respiratory tract infections in the Xuanwu District of Nanjing City can play a role in early warning and monitoring. This can provide data support for formulating response strategies and implementing prevention and control measures, transitioning from passive prevention to active prevention. Methods Study design and participant enrollment. The study was conducted in Xuanwu District, Nanjing City, from June 2023 to February 2024, jointly carried out by the Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu CDC) and the Xuanwu District Center for Disease Control and Prevention in Nanjing City. A weekly collection of influenza-like cases with respiratory infections such as fever (temperature ≥ 37°C) accompanied by cough or sore throat was done from the community. The study was approved by the Institutional Review Board of Jiangsu CDC (No. JSJK2022-B016-02). All participants have provided written informed consent for demographic characteristics, physical examinations, medical records, and sample tests. Pathogen detection Throat swab specimens during the acute phase were collected from patients (not less than 40 samples) by professional personnel following standard operating procedures. The specimens were immediately placed in sterilized sampling tubes containing 3 ml of sampling fluid and transported to the Jiangsu Provincial Center for Disease Control and Prevention laboratory within 48 hours under 4°C conditions for testing. Nucleic acid purification reagent CqEx-DNA/RNA virus (Xian Tianlong Technology Co., Ltd.) was used for nucleic acid extraction with the Tianlong GeneRotex 96 automatic nucleic acid extractor. A 16-respiratory pathogen nucleic acid detection kit produced by Beijing Zhuocheng Huisheng Biotechnology was used, and PCR amplification was performed using a fluorescence quantitative PCR instrument for respiratory pathogen nucleic acid detection. These mainly included influenza A (Flu A), influenza B (Flu B), respiratory syncytial virus(RSV), Herpes simplex virus (HSV), human adenovirus (HHADV), Enteroviruses (EV), human coronavirus (HCoV), Parainfluenza virus (HPIV), Human rhinovirus (HRV), human Bocavirus (HBoV), Human metapneumovirus(HMPV), Streptococcus pneumoniae ( S. pneumoniae ), Haemophilus influenzae( H.influenzae ), Mycoplasma pneumoniae ( M. Pneumonia ). Chlamydia pneumoniae ( C. pneumoniae ), and testing for their typing. Data were analyzed using GraphPad Prism 9.5.0 and SPSS version 27.0 software (IBM, New York, USA). This study conducted a pathogen composition analysis of various respiratory pathogens, identifying the predominant pathogen genotypes at present, to provide a basis for further control of respiratory infectious diseases. For EV and HRV typing, the respiratory multi-pathogen detection kit (Shuo Shi, JC20302) is used for the initial screening of RNA from the specimens. Positive samples for enterovirus detection undergo serotyping using the enterovirus 71 type, coxsackievirus A16 type RNA detection kit (Shuo Shi, JC20302), and coxsackievirus A6 type, A10 type RNA detection kit (Shuo Shi, JC20205). Non-EV71/CVA16/CVA6/CVA10 specimens undergo sequencing typing, with amplification primer sequences as follows: OL68-1:5`-GGTAAYTTCCACCACCANCC-3’ andEVP2:5`- CCTCCGGCCCCTGAATGCGGCT AAT-3’. PCR reaction conditions are set at 50°C for reverse transcription for 30 minutes, 95°C for denaturation for 15 minutes, followed by 35 cycles of 95°C for 30 seconds, 52°C for 45 seconds, 72°C for 90 seconds, and a final extension at 72°C for 5 minutes. The amplified products are validated using QIAxcel capillary electrophoresis before being sent to Shanghai Sangon Biotechnology Co., Ltd. for sequencing. ARIMA model The ARIMA model, as one of the common methods in time series analysis, reflects the development trend of time series data from the perspective of autocorrelation. It combines three components: autoregressive (AR), differencing (I), and moving average (MA), to capture trends and seasonal information in time series data. It can be used to forecast the incidence of respiratory infectious diseases. In this study, an ARIMA model was constructed based on weekly incidence data from June 2023 to February 2024, the Augmented Dickey-Fuller (ADF) test was performed using Eviews 12.0, using SPSS 27.0 for processing. The steps are as follows:(1). Data preprocessing: First, check for missing values in the data and replace them if necessary. Then, define the time and create a time series for the original data. (2). Stationarity identification: Identify the stationarity of the time series based on scatter plots, autocorrelation function, and partial autocorrelation function plots. Stationary non-stationary time series data until the autocorrelation function and partial autocorrelation function values are significantly non-zero. (3). Build ARIMA model: Establish the corresponding time series model based on the identified features, select the best-fitting model, and check if the residual sequence is a white noise sequence. (4). Use the validated model for forecasting. Results Demographic results Samples were collected weekly from June 2023 to February 2024, totaling 2046 samples. Among them, 1092 samples tested positive, resulting in an overall positive rate of 53.67%. The mean age was 36.8 ± 20.6 years, with the highest number of individuals in the 14–59 age group (733, 67.12%). There were 467 males (42.77%) and relatively more females (625, 57.23%). Among them, 125 individuals tested positive for mixed infections, accounting for 11.45%, while 968 individuals had single infections, accounting for 88.48%. The most common symptoms were cough 874 cases (79.60%), Sore throat 640 cases (58.29%), and Fatigue 516 cases (46.99%). (Table 1 ) Table 1 Demographic and clinical characteristics of positive cases Variable N(%) Clinical Symptoms N(%) Mean Age ± SD 36.8 ± 20.6 years Cough 874(79.60) Age Group Headache 479(43.62) 59 211(19.32) Nasal congestion 388(35.34) Gender Runny nose 504(45.90) Male 467(42.77) Fatigue 516(46.99) Female 625(57.23) Pathogen detection Simple infection 967(88.55) Mixed infection 125(11.45) ※P<0.05 shows that the difference is significant 503 cases (24.00%), influenza B virus 224 cases (10.95%), and HCoV 95 cases (4.64%) Respiratory virus infection rate The most common pathogen was Flu A, with a positive rate of 24.00% (503 cases), all are H3N2 subtypes. This was followed by Flu B at 10.95% (224 cases), all of which were typed as B.Victoria. HCoV accounted for 4.64% (95 cases), including 33 cases of HCoV-229E, 50 cases of HCoV-OC43, 11 cases of HCoV-HKU1, and 1 case of HCoV-NL63. HRV accounted for 4.06% (83 cases), sequencing of rhinovirus with simple infection, including 18 cases of Rhinovirus A, 3 cases of Rhinovirus B, and 17 cases of Rhinovirus C. HMPV accounted for 3.81% (78 cases). HPIV accounted for 3.2% (65 cases), RSV for 2.25% (46 cases) (including 14 cases of RSVA ON1 and 32 cases of RSVB BA9), HADV for 2.0% (41 cases), HSV for 1.2% (29 cases), EV for 0.24% (5 cases) (including 1 case of CVA21, 3 cases of CVA6, and 1 case of D68). M. Pneumonia accounted for 1.08% (22 cases), H.influenzae for 0.7% (15 cases), S. pneumoniae for 0.34% (7 cases), HBoV for 0.1% (2 cases), and C. pneumoniae for 0.1% (2 cases). M.Pneumonia, and HADV infections are more common in children under 14 years old, while Flu A, Flu B, HCoV, and HRV infections mainly occur in the 14–59 age group, with elderly individuals being more susceptible to HMPV infections. (Table 2 ) Table 2 Detection of pathogens varies across different age groups Pathogens 59 years ( n ,%) χ 2 P value HBoV 0(0.00) 2(100.00) 0(0.00) 0.578 * - HSV 2(7.41) 18(66.67) 7(25.93) 1.324 * 0.485 S.pneumoniae 0(0.00) 5(71.43) 2(28.57) 0.977 * 0.619 C.pneumoniae 0(0.00) 2(100.00) 0(0.00) 0.578 * - M.Pneumonia 12(54.55) 10(45.45) 0(0.00) 23.693 * < 0.001 H.influenzae 3(20.00) 9(60.00) 3(20.00) 0.909 * 0.573 HMPV 8(10.96) 33(45.21) 32(43.84) 30.328 < 0.001 HADV 25(60.98) 13(31.71) 3(7.32) 80.133 < 0.001 Flu A 48(10.23) 336(71.64) 85(18.12) 10.665 0.005 Flu B 38(19.29) 138(70.05) 21(10.66) 14.998 0.001 EV 0(0.00) 5(100.00) 0(0.00) 1.167 * 0.508 HPIV 7(11.11) 43(68.25) 13(20.63) 0.417 0.822 HCoV 4(4.40) 59(64.84) 28(30.77) 13.133 0.002 HRV 8(9.64) 55(66.27) 20(24.09) 2.174 0.337 RSV 4(8.70) 34(73.91) 8(17.39) 1.333 0.554 ※P<0.05 shows that the difference is significant * Denotes Fisher exact probability method Except for RSV, all other pathogens can cause symptoms such as cough, headache, sore throat, muscle pain, nasal congestion, runny nose, and fatigue. Among these, H. influenzae (43, 21.83%), HMPV (59, 24.08%), HADV (38, 30.16%), Flu A (380, 22.77%), HPIV (23, 23.96%), and HCoV (165, 24.59%) infections commonly present with cough as the predominant symptom. HPIV (20, 20.83%) is the pathogen most likely to cause sore throat, while runny nose is most commonly associated with HADV infection (21, 16.67%). Please refer to Table 3 for details. Table 3 Clinical Symptom Distribution Characteristics of Different Pathogen Infections Pathogens Cough Headache Sore throat Muscle pain Nasal congestion Runny nose Fatigue χ 2 P value HRV 49(20.16) 30(12.35) 47(19.34) 25(10.29) 27(11.11) 37(15.23) 28(11.52) 19.54 0.003 EV 3(15.00) 3(15.00) 2(10.00) 3(15.00) 3(15.00) 2(10.00) 4(20.00) 1.419* 0.996 HSV 19(19.39) 14(14.29) 13(13.27) 10(10.20%) 14(14.29) 15(15.31) 13(15.00) 3.667 0.738 S.pneumoniae 5(25.00) 3(15.00) 3(15.00) 2(10.00) 1(5.00) 3(15.00) 3(15.00) 3.596* 0.768 M.Pneumonia 20(26.67) 11(14.67) 10(13.33) 7(9.33) 9(12.00) 10(13.33) 8(10.67) 12.133 0.060 HPIV 43(21.83) 22(11.17) 33(16.75) 19(9.64) 24(12.18) 30(15.23) 26(13.20) 16.203 0.013 HCoV 59(24.08) 34(13.88) 48(19.59) 25(10.20) 22(8.98) 27(11.02) 30(12.24) 38.8 < 0.001 RSV 38(30.16) 12(9.52) 23(18.25) 4(3.17) 17(13.49) 21(16.67) 11(8.73) 46.407 < 0.001 Flu A 380(22.77) 214(12.82) 280(16.78) 153(9.17) 163(9.77) 225(13.48) 254(15.22) 175.066 < 0.001 H.influenzae 12(19.35) 8(12.90) 12(19.35) 7(11.29) 6(9.68) 7(11.29) 10(16.13) 4.855 0.582 HMPV 55(26.57) 23(11.11) 36(17.39) 18(8.70) 22(10.63) 31(14.98) 22(10.63) 38.731 < 0.001 HADV 23(23.96) 15(15.63) 20(20.83) 11(11.46) 7(7.29) 8(8.33) 12(12.50) 18.326 0.005 Flu B 165(24.59) 87(12.97) 112(16.69) 60(8.94) 70(10.43) 85(12.67) 92(13.71) 87.714 < 0.001 HBoV 1(10.00) 2(20.00) 1(10.00) 1(10.00) 2(20.00) 2(20.00) 1(10.00) 1.856* - C.pneumoniae 2(25.00) 1(12.50) 0(0.00) 1(12.50) 1(12.50) 1(12.50) 2(12.50) 3.227 0.964 ※P<0.05 shows that the difference is significant * Denotes Fisher exact probability method The positive rate for mixed infections was 6.11% (125 out of 2046), with double infection accounting for 5.82% (119/2046), triple infection for 0.24% (5/2046), and quadruple infection for 0.05% (1/2046). The detection rate of Flu A + HRV is the highest in mixed infections at 20.69% (24 cases) followed by FluA + HCoV at 8.62%, (10 cases) and FluA + HSV at 11.11% (9 cases) (Fig. 2 ). Flu A was mostly co-infected with other pathogens. Simultaneous or sequential infection of respiratory pathogens may lead to mixed infections, causing antagonistic or synergistic effects among pathogens and altering the severity of the disease. Comparative analysis was conducted between mixed infections and single infections based on Ct values, gender, age, and number of symptoms. (Table 4 ). In the cases of single infection, there were 585 females (58.15%) and 421 males (41.85%). For double-mixed infections, there were 49 males (41.17%) and 70 females (58.82%). In cases of multiple mixed infections, there were 2 males (33.33%) and 4 females (66.67%). The differences were not statistically significant (p > 0.05).In different age groups, there was no statistical significance between simple infection and co-infection in the age group, and age did not affect co-infection. The pathogens in both simple and mixed infections most commonly caused cases to exhibit two symptoms, with 235 cases (24.38%) for simple infection and 34 cases (27.20%) for mixed infection. However, there was no statistically significant difference in the number of symptoms caused by the infection situation(p > 0.05). the ct value of simple infection HSV was larger, and the difference in Ct value was statistically significant. Table 4 Comparison of demographic characteristics and number of symptoms between mixed and single infection Single infection(N,%) Co-infection(N,%) 2 >2 χ 2 P value Gender Male 421(41.85) 49(41.17) 2(33.33) 0.207 0.976* Female 585(58.15) 70(58.82) 4(66.67) Age group(yr) 59 Number of symptoms 186(18.49) 25(21.01) 0(0.00) 1 216(22.50) 22(18.49) 1(16.67) 10.189 0.496* 2 235(24.48) 33(27.73) 1(16.67) 3 156(16.25) 20(16.81) 2(33.33) 4 110(11.46) 14(11.76) 0(0.00) 5 102(10.62) 8(6.72) 0(0.00) 6 61(6.35) 9(7.56) 2(33.33) 7 80(8.33) 13(10.92) 0(0.00) ※P<0.05 shows that the difference is significant * Denotes Fisher exact probability method Table 5 Comparison of Ct values between mixed and single infection Pathogens Simple infection(N,%) Mixed infection(N,%) t P value HRV 29.67 28.93 0.919 0.057 EV 31.10 32.16 0.568 0.806 HSV 34.57 32.55 1.661 0.042 S. pneumoniae 34.47 32.55 0.605 0.936 M. Pneumonia 33.34 27.62 4.577 0.666 HPIV 27.19 27.43 2.344 0.723 HCoV 29.38 29.65 0.206 0.685 RSV 31.16 31.58 0.25 0.086 Flu A 27.19 27.43 0.345 0.423 H. influenzae 32.14 32.94 0.107 0.213 HMPV 27.79 29.21 1.047 0.727 HADV 31.13 32.84 1.060 0.824 Flu B 27.15 27.17 0.019 0.213 ※P<0.05 shows that the difference is significant Information on mixed infections in this study. (Table 4 ): Difference in Ct values between mixed and simple infections. The study data was compared with respiratory pathogen surveillance data from Beijing [ 13 ] and Jinan [ 14 ] during the NPIS. In Beijing, the total positive detection rate was 10.97% during the NPIS period. The top five pathogen positives, from highest to lowest, were HCoV (2.42%), HRV (2.17%), HPIV (1.71%), Flu A and Flu B (1.50%), and RSV (1.23%). In Jinan, the overall positive detection rate was 40.18%. Among the top five pathogens, the positive rates were 9.85% for HRV, 8.94% for M. Pneumonia , 6.53% for RSV, 3.13% for HPIV, and 2.16% for HADV. In this study, the positive detection rate began to increase in week 38 of 2023 (September 11–17) and peaked in week 41 (October 2–8), with the highest number of positive cases recorded in week 48 (November 20–26) (Fig. 3 A). Influenza maintained a high level throughout the monitoring period. Flu A detection peaked in week 48 (November 20–26) with a positive rate of 62.6% and remained high from week 41, 2023 to week 1, 2024 (October 2-January 7). Flu B showed a high rate from week 52, 2023 to week 6, 2024 (December 18,2023-February 11, 2024). HRV and HCoV detection peaked in week 39 (September 18–24) and then gradually declined (Fig. 3 B). EV, HPIV, HMPV, and RSV were detected throughout the monitoring period but with relatively low detection rates. HHADV had a relatively higher detection rate from week 52, 2023 to week 6, 2024, while H. influenzae and S. pneumoniae were detected in weeks 49–51 (November 27-December 17) and were not detected during other times. Building the ARIMA Model Stabilizing the Series A model was established based on weekly incidence data from June 2023 to February 2024, and a time series plot was generated. The time series plot showed non-stationarity. Next, the stationarity of the series was confirmed using the Augmented Dickey-Fuller (ADF) unit root test in Eviews software, after conducting trend and intercept tests, with a reported t=-3.37, p-value > 0.05. Thus, the original time series required stabilization. After applying a first-order difference (d = 1), a transformed time series plot was generated. It was visually assessed for stationarity and tapering, indicating improved stationarity. The ADF unit root test yielded a p-value < 0.01, confirming basic stationarity. However, seasonal differencing led to increased instability, so a first-order difference was chosen[Supplementary Fig. 1]. Model Identification ACF and PACF plots were generated for the transformed series, showing zero-lag autocorrelation and partial autocorrelation after first-order differencing. ARIMA (0,1,4) (0,0,0) was selected as the optimal model after testing different p, d, and q values. The model had an R2 value of 0.930, and the standardized Bayesian Information Criterion (BIC) value was the smallest among all fitted models at 5.338. A residual test using Ljung-Box Q = 10.930 and p = 0.691 confirmed that the residual sequence was white noise. The mean absolute percentage error (MAPE) between actual and predicted values was 34.181, indicating a good model fit[Supplementary Fig. 2]. Model Fitting Based on the weekly number of cases from June 2023 to February 2024, the ARIMA (0,1,4) (0,0,0) model was constructed. The optimal model was used to predict the number of respiratory pathogens until June 2024, as shown in the graph (Fig. 4 ). The actual number of cases, as seen from the table, all fall within the 95% confidence interval of the predicted values, indicating a good fit of the model. Table 6 ARIMA Model Fitting of positive detection rates for pathogens between Nov.2023 and Feb.2024 Date Actual Fitting 95%CI 20 ~ 26 November 2023 146 151.98 132.99 ~ 170.96 27 November – 3 December 2023 36 61.86 43.00 ~ 80.72 4–10 December 2023 20 28.59 9.73 ~ 47.44 11–17 December 2023 25 28.08 9.23 ~ 46.93 18–24 December 2023 48 47.52 28.76 ~ 66.29 25–31 December 2023 53 47.4 28.65 ~ 66.15 1–7 January 2024 40 50.38 31.63 ~ 69.12 8–14 January 2024 56 46.36 27.67 ~ 65.05 15–21 January 2024 92 94.94 76.28 ~ 113.6 22–28 January 2024 63 55.59 36.93 ~ 74.24 29 January − 4 February 2024 45 45.12 26.48 ~ 63.75 Discussion Large and medium-sized cities have a concentrated population, obvious seasonal climate, and high incidence of respiratory infectious diseases. This study focused on monitoring multiple respiratory pathogens in feverish populations in Nanjing communities to establish a respiratory pathogen spectrum and fill the gap in acute respiratory infectious disease monitoring after the cessation of NPIs. The study showed that from week 42 (October 2023), there was a rapid increase in positive pathogen samples, maintaining a stable and high positive detection rate, dropping in December, and then showing a growth trend until early February 2024. This study conducted molecular epidemiological research on respiratory pathogens to identify the currently dominant genotypes, which contributes to a deeper understanding of the epidemiological characteristics of different pathogens, including their transmission routes, infectivity, and seasonal distribution. The overall positive detection rate in this study was 53.57%, with high detection rates for Flu A and Flu B (24.00% and 10.95%, respectively). All Flu A subtypes were H3N2, while all Flu B subtypes were B/Victoria lineage. Influenza viruses showed a peak in winter and spring, with Flu A prevalent from October to January and Flu B prevalent from December to February, consistent with research in Beijing [ 15 ] . However, the high detection rate of influenza viruses in our study indicates a serious influenza situation in our city. HRV was prevalent from August to October, consistent with research in Taizhou [ 16 ] . This study conducted typing tests on simple infection of rhinoviruses and enteroviruses, detecting a total of 18 strains of rhinovirus A, including 3 of type A21, 2 of type A64, 2 of type A7, 1 of type A71, and 3 of type A98, as well as 3 strains of rhinovirus B, including 1 of type B69, and 17 strains of rhinovirus C, including 2 of type C1. It can be observed that the detection rates of HRV A and C types are relatively high, consistent with domestic and international research results [ 17 – 18 ] . HRV-A and HRV-C are more likely to cause moderate to severe diseases, and type C is associated with childhood asthma attacks. Therefore, strengthening the monitoring of rhinovirus typing is of great significance for the prevention and control of respiratory infectious diseases. In enteroviruses, a total of 3 strains of coxsackievirus A6, 1 strain of coxsackievirus A21, and 1 strain of enterovirus D68 were detected. Coxsackievirus A6 has become one of the main pathogens causing human hand-foot-and-mouth disease in recent years. While coxsackievirus A21 and enterovirus D68 can also enter the body through the respiratory tract, they rarely cause hand-foot-and-mouth disease and herpangina, mainly manifesting as symptoms of upper respiratory tract infections. HPIV, HCoV, HSV, EV, and HADV showed short-term low prevalence. Among them, HPIV is mainly type HPIV-3, and among the four serotypes, the infection rate of HPIV-3 is the highest, often peaking in the winter and spring seasons, consistent with previous research findings [ 19 ] . HCoV mainly invaded the upper respiratory tract with HCoV-OC43 and HCoV-229E types. HMPV, H. influenzae , S. pneumoniae , and C. pneumoniae had relatively low annual detection rates, appearing sporadically. However, due to the short duration of our study and the lack of coverage in the spring, it is not sufficient to fully describe the common respiratory pathogen prevalence characteristics in the region, requiring further supplementation in the future. Mixed infections of pathogens may pose challenges to the diagnosis, treatment, and epidemic prevention and control of respiratory infections. Concurrent or sequential infection of respiratory pathogens may lead to mixed infections, causing positive synergistic or negative antagonistic interactions among pathogens, leading to varying degrees of disease severity changes in patients. In this study, mixed infections accounted for 11.45% of total positive cases, with Flu A mostly co-infected with other pathogens, and the highest positive detection rates in mixed infections were observed for Flu A + HRV, FluA + HCoV, FluA + HSV. Previous studies suggested negative interactions between IAV and RSV, HRV and IAV, while RSV and HRV co-infections indicated increased disease severity [ 20 ] . Previous studies have shown that [ 21 ] ,co-infections may lead to an increased hospitalization rate among patients with respiratory viral infections, indicating an escalation in disease severity. This study conducted a comparative analysis of single infections and co-infections based on gender, different age groups, Ct values, and the number of symptoms. The results ultimately revealed statistically significant differences between single infections and co-infections across different age groups. The lack of statistical significance in symptom numbers may be due to the challenge of deriving conclusions about severity solely based on symptom counts. HRV, HSV, M. Pneumoniae , and S. pneumoniae in mixed infections had smaller Ct values compared to single infections, possibly due to synergistic effects between pathogens, resulting in increased disease severity. The Ct value for HSV single infection was 34.57, while for mixed infection, it was 32.55, with significant differences and higher persuasiveness. However, HADV and HMPV single infection had larger Ct values, possibly related to their role as primary infecting viruses activating the host's non-specific innate immune response. Due to the short study period and relatively low number of mixed infection cases, significant results could not be obtained. Viral interference may provide a new model for antiviral treatment research. Some studies have shown that Influenza A virus Defective Interfering Particles (IAV-DIPs) can stimulate the host's innate immune system to inhibit HSV infection and replication [ 22 ] , suggesting a potential preventive and therapeutic role in respiratory infectious diseases. There is limited research in China in this area, and future monitoring of more data can lead to further research. Subsequent follow-up tracking or research based on hospital cases could further investigate this matter. Starting from January 2023, China lifted the control measures for COVID-19 from Class A infectious diseases. This study was conducted from June 2023 to the end of February 2024, after comprehensive relaxation of epidemic control measures. The aim was to explore the changes in the respiratory pathogen spectrum after the cessation of NPIs. Since the emergence of COVID-19, China has implemented non-pharmaceutical interventions (NPIs) including encouraging mask-wearing, patient isolation, social distancing, hand hygiene, and disinfection to prevent new SARS-CoV-2 infections. Comparing the spectrum of respiratory pathogens in Jinan City and Beijing City during NPIS period with that in this study, the overall positive detection rate of pathogens in this study (54.15%) was significantly higher than that in Jinan City (40.18%) and Beijing (10.97%), indicating that NPIS measures against COVID-19 greatly reduced the prevalence of respiratory pathogens. Furthermore, the detection rate of influenza in this study was 34.95% ((24.00% + 0.95%), which was significantly higher than 3.44% in Jinan City and 1.5% in Beijing City, and the positive detection rate of all pathogens in this study was higher than that in Beijing city, possibly because NPIS measures during COVID-19 not only prevented the invasion of viruses but also cut off the transmission of other respiratory pathogens. However, the overall positive rate of respiratory pathogens is rising, which may be linked to the public's relaxation of vigilance against respiratory infectious diseases, and may also be related to the immune debt after the novel coronavirus pandemic, resulting in a rebound or high epidemic level of some infectious diseases. However, during NPIS, the positive rate of HRV pathogens in Jinan City was 9.85%, higher than 8.7% in this study, which may be since HRV is transmitted through direct or indirect contact with contaminated items, which requires chlorine-based disinfectants to eradicate, and the use of ethanol is less effective. In addition, the positive rates of Mycobacterium pneumoniae and respiratory syncytial virus in Jinan were significantly higher than in our study, which may be due to the fact that our study focussed on community populations rather than hospital-based studies, and that Jinan has a higher proportion of children under the age of 15, who are more susceptible to these pathogens. In recent years, there has been extensive research in China utilizing the ARIMA model for infectious disease surveillance and prediction, demonstrating its effectiveness, particularly in short-term forecasting [ 23 – 24 ] . Based on the scientifically predicted results of the model, early detection of respiratory pathogen trends can be achieved, providing timely warnings for control efforts and facilitating the targeted formulation of prevention and control strategies. In this study, fitting models were established using the ARIMA model (0,1,4), (0,0,0) based on influenza surveillance data from June 2023 to February 2024. According to the forecast results of the ARIMA model, influenza peaks are expected to occur in late autumn and winter of 2023, with the number of detected respiratory pathogens projected to decline initially from March to June 2024 before stabilizing. This trend may be attributed to the rising temperatures during the spring and summer seasons. Nanjing, characterized by a subtropical monsoon climate, experiences a noticeable temperature increase by the end of February along with high humidity and rainfall. Studies have indicated that the transmission of respiratory viruses is associated with climate conditions, especially humidity and temperature, with respiratory pathogens being more likely to spread under cold and dry conditions [ 25 ] . Additionally, this study has certain limitations as it only considers the quantity of detected pathogens, potentially leading to underreporting or overreporting biases in weekly data. Overall, our study monitored respiratory infections in the community population of Nanjing City, providing insights into the spectrum and co-infections of respiratory pathogens., A time series forecasting model has been established to serve as a reference for prevention and control efforts. While filling gaps in Nanjing's respiratory pathogen spectrum research, our study has limitations due to a short period and single sample source. Future research could involve hospital samples to further understand the epidemiology of respiratory pathogens, establish a more comprehensive pathogen spectrum, and enhance Nanjing's monitoring and alert system post-COVID-19 pandemic. Declarations Ethics approval and consent to participate: Our study did not require further ethics committee approval as it did not involve animal or human clinical trials and was not unethical. By the ethical principles outlined in the Declaration of Helsinki, all participants provided informed consent before participating in the study. The anonymity and confidentiality of the participants were guaranteed, and participation was completely voluntary. Consent for publication: All authors approved the final manuscript and the submission to this journal. Availability of data and materials: The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests: No conflicts of interest. Funding: This work was supported by National Natural Science Foundation of China (82222062); Jiangsu Province 333 project ; Scientific research project of Jiangsu health commission (DX202301); Social Development Foundation of Jiangsu Province (BE2021739); Science and Technology Project of Jiangsu Province (BE2023601) . Authors' contributions: Fei Deng was responsible for completing most of the experimental work and contributed to the data collation. Zhuhan Dong did all the data collection and analysis, and she wrote the first draft of the manuscript. Tian Qiu, Ke Xu, Qigang Dai, Huiyan Yu, Huan Fan, Haifeng Qian, and Changjun Bao participated in the analysis and interpretation of all data. Liguo Zhu designed the project, planned the experiments, verified the data collection, analysis and interpretation, and revised the original manuscript into the final version submitted. All authors reviewed the manuscript, agreed to take responsibility for all aspects of the work, and testified to the accuracy and completeness of the work. References GBD 2017 Influenza Collaborators. Mortality, morbidity, and hospitalizations due to influenza lower respiratory tract infections, 2017: an analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2019;7(1):69-89. Yang J, McClymont H, Wang L, Vardoulakis S, Hu W. Epidemic Features of COVID-19 and Potential Impact of Hospital Strain During the Omicron Wave - Australia, 2022. China CDC Wkly. 2023;5(7):165-169. Jin X, Ren J, Li R, et al. Global burden of upper respiratory infections in 204 countries and territories, from 1990 to 2019. EClinicalMedicine. 2021;37:100986. ZHOU Le, HAN Zhuan-zhuan, ZHANG Xiu-ling, XU Rong-rong, CHEN Yi - fei, CHEN Xiang. Detection and analysis of 22 respiratory pathogens in Yangzhou area. 2023; 33(18): 2191-2194, 2202. Zhang J, Yang T, Zou M, Wang L, Sai L. The epidemiological features of respiratory tract infection using the multiplex panels detection during COVID-19 pandemic in Shandong province, China. Sci Rep. 2023;13(1):6319. ZHAO Ping, CUl Geng-li, Wu Xiao-xue. Pathogenic spectrum analysis of adult cases of respiratory tract infection in Beijing, 2017-2020. South China Journal of Preventive Medicine, 2022,37(05):45-47+73. Ye C, Zhang G, Zhang A, et al. The Omicron Variant Reinfection Risk among Individuals with a Previous SARS-CoV-2 Infection within One Year in Shanghai, China: A Cross-Sectional Study. Vaccines (Basel). 2023;11(7):1146. Liu P, Xu M, Cao L, et al. Impact of COVID-19 pandemic on the prevalence of respiratory viruses in children with lower respiratory tract infections in China. Virol J. 2021;18(1):159. Shi HJ, Kim NY, Eom SA, et al. Effects of Non-Pharmacological Interventions on Respiratory Viruses Other Than SARS-CoV-2: Analysis of Laboratory Surveillance and Literature Review From 2018 to 2021. J Korean Med Sci. 2022;37(21):e172. Yan H, Zhai B, Yang F, Wang P, Zhou Y. The Impact of Non-pharmacological Interventions Measures Against COVID-19 on Respiratory Virus in Preschool Children in Henan, China. J Epidemiol Glob Health. 2024;14(1):54-62. HU Xiao-su, SHAO Yi, ZHANG Wen-li. CHEN Kang, GAO Yue. Characteristic Analysis and Prediction of Hospital Influenza-Like Illness Based on ARIMA Mode. Hospital Management Forum, 2023,40(12):10-13+64. Claris S, Peter N. ARIMA MODEL IN PREDICTING OF COVID-19 EPIDEMIC FOR THE SOUTHERN AFRICA REGION. Afr J Infect Dis. 2022;17(1):1-9 Zhang J, Yang T, Zou M, Wang L, Sai L. The epidemiological features of respiratory tract infection using the multiplex panels detection during COVID-19 pandemic in Shandong province, China. Sci Rep. 2023;13(1):6319. Dong M, Luo M, Li A, et al. Changes in the pathogenic spectrum of acute respiratory tract infections during the COVID-19 epidemic in Beijing, China: A large-scale active surveillance study. J Infect. 2021;83(5):607-635. Zou Lin, Gao Xiang, Zhang Chong, et al. Epidemiological characteristics of respiratory pathogen infection in patients with respiratory tract infection in Tongzhou district, Beijing from 2020 to 2022. Disease Surveillance, 2021,47(03):296-300. DING Wen, ZHA Jie, MA Zhi Pathogen monitoring results of people with acute respiratory infection in Taizhou, 2016-2017. Modern Preventive Medicine. 2019,46(13):2436-2439+2444. Yang Kang, XIE Jiamin, HUANG Xinxin, et al. Prevalence and Molecular Typing of Rhinoviruses in People with Severe Acute Infection of the Respiratory Tract in Guangdong Province, China,2019-2021. Chin J Virol, 2023, 39(02):364-371. Li Wanwei, Yu Bo, Zhou Jijian, et al. Genetic diversity and epidemiology of human rhinovirus among children with severe acute respiratory tract infection in Guangzhou, China. Virol J,2021, 18(1):174. Parsons J, Korsman S, Smuts H, et al. Human Parainfluenza Virus (HPIV) Detection in Hospitalized Children with Acute Respiratory Tract Infection in the Western Cape, South Africa during 2014-2022 Reveals a Shift in Dominance of HPIV 3 and 4 Infections. Diagnostics (Basel). 2023;13(15):2576. Piret J, Boivin G. Viral Interference between Respiratory Viruses. Emerg Infect Dis. 2022;28(2):273-281. Asner SA, Science ME, Tran D, Smieja M, Merglen A, Mertz D. Clinical disease severity of respiratory viral co-infection versus single viral infection: a systematic review and meta-analysis. PLoS One. 2014;9(6):e99392. Pelz L, Piagnani E, Marsall P, et al. Broad-Spectrum Antiviral Activity of Influenza A Defective Interfering Particles against Respiratory Syncytial, Yellow Fever, and Zika Virus Replication In Vitro. Viruses. 2023;15(9):1872. Tang lin,Lv wenli, Bao lili, el al. Predictive Analysis of Influenza-like Cases in Chifeng Based on ARIMA Model Predictive Analysis. Chinese Journal of Social Medicine. 2023,40(03):350-354. Zou Xiaojiang, Zhao Han, Wang Qiyin, Ye Mengliang. Prediction of influenza in Chongqing, China, based on the Autoregressive Integrated Moving Average model. Journal of Chongqing Medical University. 2023,48(12):1425-1429. Sloan C, Moore ML, Hartert T. Impact of pollution, climate, and sociodemographic factors on spatiotemporal dynamics of seasonal respiratory viruses. Clin Transl Sci. 2011;4(1):48-54. Additional Declarations No competing interests reported. Supplementary Files Attachments.docx Cite Share Download PDF Status: Published Journal Publication published 20 Sep, 2024 Read the published version in Virology Journal → Version 1 posted Reviews received at journal 15 Aug, 2024 Reviews received at journal 09 Aug, 2024 Reviewers agreed at journal 05 Aug, 2024 Reviews received at journal 04 Aug, 2024 Reviewers agreed at journal 31 Jul, 2024 Reviewers agreed at journal 31 Jul, 2024 Reviewers agreed at journal 30 Jul, 2024 Reviewers invited by journal 30 Jul, 2024 Editor assigned by journal 01 Jul, 2024 Submission checks completed at journal 01 Jul, 2024 First submitted to journal 27 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4645900","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324522879,"identity":"0ba6222e-43da-444a-a0f1-4b54dca8f753","order_by":0,"name":"Fei Deng","email":"","orcid":"","institution":"Jiangsu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Deng","suffix":""},{"id":324522881,"identity":"688c4433-9adc-4a3f-b7f6-9f83e666252f","order_by":1,"name":"Zhuhan Dong","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhuhan","middleName":"","lastName":"Dong","suffix":""},{"id":324522882,"identity":"34c436c9-f7f9-468d-88f1-43d555b2c922","order_by":2,"name":"Tian Qiu","email":"","orcid":"","institution":"Xuanwu District Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Tian","middleName":"","lastName":"Qiu","suffix":""},{"id":324522884,"identity":"62f60fdc-bf5e-462e-bd85-4d839cf5bce8","order_by":3,"name":"Ke Xu","email":"","orcid":"","institution":"Jiangsu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Xu","suffix":""},{"id":324522886,"identity":"4555a9b7-c3a7-4fd1-9b7a-965ff07dfc1b","order_by":4,"name":"Qigang Dai","email":"","orcid":"","institution":"Jiangsu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Qigang","middleName":"","lastName":"Dai","suffix":""},{"id":324522888,"identity":"511104b4-5b9a-412d-8e53-7cdf959e09f9","order_by":5,"name":"Huiyan Yu","email":"","orcid":"","institution":"Jiangsu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Huiyan","middleName":"","lastName":"Yu","suffix":""},{"id":324522892,"identity":"dee1155a-e924-4977-992a-5af4be658347","order_by":6,"name":"Huan Fan","email":"","orcid":"","institution":"Jiangsu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Fan","suffix":""},{"id":324522893,"identity":"c281e860-ba37-4620-a66a-e78cb610482e","order_by":7,"name":"Haifeng Qian","email":"","orcid":"","institution":"Xuanwu District Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Haifeng","middleName":"","lastName":"Qian","suffix":""},{"id":324522896,"identity":"d0adb818-4aa4-43e9-827c-1ec1d2494e26","order_by":8,"name":"Changjun Bao","email":"","orcid":"","institution":"Jiangsu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Changjun","middleName":"","lastName":"Bao","suffix":""},{"id":324522898,"identity":"a61ca0c9-96b3-474a-a08c-a9cfc021ae24","order_by":9,"name":"Wei Gao","email":"","orcid":"","institution":"Xuanwu District Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Gao","suffix":""},{"id":324522900,"identity":"e6366fac-4fe2-44ca-ad6c-66e6d02df4ff","order_by":10,"name":"Liguo Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIie3PsYrCQBCA4RkCplnOdg9BX2GOwHFg0FdxCazNFj5CJKBNuDpw9xD6BovDpfIBLCy85mrLICmMAcEqSSnc/s1MMV8xAC7XMyYB7ayafQ+XpzOFky4EavK6TvgtW+ioG7lN2ud6IM47jNvE6Csle7rwEA6GBiFZD3z+2TQR/N6TVZ8cYGYoMHR8AaH1oYl40lQkZZVUS2TozwMp3htJ705W1cIfxBi3EXEjs4JVKnKVQBcipV5YFc8D6SeMKemo1/bLKIu2v0U5Hk4Z10VRhpO+z3kjqcPV43et53VltzOXy+X6p10B4b5Qegnr4uUAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Liguo","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-06-27 04:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4645900/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4645900/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12985-024-02494-9","type":"published","date":"2024-09-20T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60943937,"identity":"6bbb0377-fa28-4611-93d1-fda0312f5d8d","added_by":"auto","created_at":"2024-07-23 22:04:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58127,"visible":true,"origin":"","legend":"\u003cp\u003eInformation on weekly changes in this study. Positive detection rate and number per week in this study,\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4645900/v1/0a08e81c93eaefffb517b950.jpg"},{"id":60944649,"identity":"d8536445-2f54-441e-84c9-4b328ee88ddf","added_by":"auto","created_at":"2024-07-23 22:12:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48088,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of different types of mixed infection\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4645900/v1/d22f8d22242e87c4c42bdaca.jpg"},{"id":60943939,"identity":"3be6623a-a42f-4957-8e26-b1df92324ca7","added_by":"auto","created_at":"2024-07-23 22:04:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48059,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in weekly positive detection rates for pathogens.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4645900/v1/20310fd9d0a7e1e9cab20584.jpg"},{"id":60943940,"identity":"baa17f7c-eec6-4a4c-a18a-890600c09633","added_by":"auto","created_at":"2024-07-23 22:04:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31025,"visible":true,"origin":"","legend":"\u003cp\u003eThe ARIMA Model Fitting\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4645900/v1/db17a08bc96c839203f858db.jpg"},{"id":65431725,"identity":"8b4241f3-bba0-4623-8451-4b22c00b8a62","added_by":"auto","created_at":"2024-09-27 11:59:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":942540,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4645900/v1/d8654c09-da96-41d0-93a5-a6511772fd7b.pdf"},{"id":60944650,"identity":"cbbc6b93-465b-4270-88a0-57a406980263","added_by":"auto","created_at":"2024-07-23 22:12:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":83479,"visible":true,"origin":"","legend":"","description":"","filename":"Attachments.docx","url":"https://assets-eu.researchsquare.com/files/rs-4645900/v1/d9586e2773d56f44ea4ae0c0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Respiratory Pathogen Dynamics in Community Fever Cases — Jiangsu Province, China (2023-2024)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal Burden of Disease (GBD) studies show that respiratory infectious diseases cause approximately 145,000 deaths globally every year\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, in 2019, the number of cases of upper respiratory tract infection was as high as 17.2\u0026nbsp;billion, accounting for 42.83% of all disease cases in the GBD database, and the incidence rate was the highest\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, posing a huge threat to global human health. Currently, respiratory tract infections rank second in the global burden of disease among children and adolescents, making it one of the leading causes of rising morbidity and mortality worldwide\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The susceptible population of respiratory infectious diseases is wide, the transmission route is easy to achieve, and often caused by a variety of pathogens, and the infection situation is complex and often overlapping, so the detection work is often very tedious\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Currently, there is more research in China on monitoring and controlling novel coronaviruses and influenza viruses\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, with less focus on monitoring other pathogens. This study conducts multi-pathogen testing on feverish populations in Xuanwu District, Nanjing City, establishes a local respiratory pathogen spectrum, understands the epidemiological patterns of pathogens, and provides more basis for the diagnosis and treatment of respiratory infections in patients with unknown pathogens in clinical practice.\u003c/p\u003e \u003cp\u003eFrom 2019 to 2023, the world experienced a pandemic of the novel coronavirus (COVID-19). As the virulence of COVID-19 weakened, community transmission decreased, and vaccination coverage became more widespread, the severity of COVID-19 appeared to decrease. However, it still poses a threat due to its strong infectivity. COVID-19 prevention and control remain a hot topic in the field of public health\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In recent years, China and other countries have explored the impact of non-pharmaceutical interventions (NPIs)\u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e related to novel coronavirus on the prevalence of respiratory infectious diseases. However, most studies have not included the post-NPIS period. This study aims to establish the current respiratory pathogen spectrum and compare the impact and changes in respiratory infections after the cessation of NPIs.\u003c/p\u003e \u003cp\u003eIn recent years, there has been an increasing trend in using mathematical models for infectious disease early warning and prediction\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. However, the direction of respiratory tract infection prediction remains relatively underexplored. Establishing effective and accurate predictive models to forecast the future trends of respiratory tract infections in the Xuanwu District of Nanjing City can play a role in early warning and monitoring. This can provide data support for formulating response strategies and implementing prevention and control measures, transitioning from passive prevention to active prevention.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cem\u003eStudy design and participant enrollment.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe study was conducted in Xuanwu District, Nanjing City, from June 2023 to February 2024, jointly carried out by the Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu CDC) and the Xuanwu District Center for Disease Control and Prevention in Nanjing City. A weekly collection of influenza-like cases with respiratory infections such as fever (temperature\u0026thinsp;\u0026ge;\u0026thinsp;37\u0026deg;C) accompanied by cough or sore throat was done from the community. The study was approved by the Institutional Review Board of Jiangsu CDC (No. JSJK2022-B016-02). All participants have provided written informed consent for demographic characteristics, physical examinations, medical records, and sample tests.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePathogen detection\u003c/h2\u003e \u003cp\u003eThroat swab specimens during the acute phase were collected from patients (not less than 40 samples) by professional personnel following standard operating procedures. The specimens were immediately placed in sterilized sampling tubes containing 3 ml of sampling fluid and transported to the Jiangsu Provincial Center for Disease Control and Prevention laboratory within 48 hours under 4\u0026deg;C conditions for testing.\u003c/p\u003e \u003cp\u003eNucleic acid purification reagent CqEx-DNA/RNA virus (Xian Tianlong Technology Co., Ltd.) was used for nucleic acid extraction with the Tianlong GeneRotex 96 automatic nucleic acid extractor. A 16-respiratory pathogen nucleic acid detection kit produced by Beijing Zhuocheng Huisheng Biotechnology was used, and PCR amplification was performed using a fluorescence quantitative PCR instrument for respiratory pathogen nucleic acid detection. These mainly included influenza A (Flu A), influenza B (Flu B), respiratory syncytial virus(RSV), Herpes simplex virus (HSV), human adenovirus (HHADV), Enteroviruses (EV), human coronavirus (HCoV), Parainfluenza virus (HPIV), Human rhinovirus (HRV), human Bocavirus (HBoV), Human metapneumovirus(HMPV), Streptococcus pneumoniae (\u003cem\u003eS. pneumoniae\u003c/em\u003e), Haemophilus influenzae(\u003cem\u003eH.influenzae\u003c/em\u003e), Mycoplasma pneumoniae (\u003cem\u003eM. Pneumonia\u003c/em\u003e). Chlamydia pneumoniae (\u003cem\u003eC. pneumoniae\u003c/em\u003e), and testing for their typing. Data were analyzed using GraphPad Prism 9.5.0 and SPSS version 27.0 software (IBM, New York, USA).\u003c/p\u003e \u003cp\u003eThis study conducted a pathogen composition analysis of various respiratory pathogens, identifying the predominant pathogen genotypes at present, to provide a basis for further control of respiratory infectious diseases. For EV and HRV typing, the respiratory multi-pathogen detection kit (Shuo Shi, JC20302) is used for the initial screening of RNA from the specimens. Positive samples for enterovirus detection undergo serotyping using the enterovirus 71 type, coxsackievirus A16 type RNA detection kit (Shuo Shi, JC20302), and coxsackievirus A6 type, A10 type RNA detection kit (Shuo Shi, JC20205). Non-EV71/CVA16/CVA6/CVA10 specimens undergo sequencing typing, with amplification primer sequences as follows: OL68-1:5`-GGTAAYTTCCACCACCANCC-3\u0026rsquo; andEVP2:5`- CCTCCGGCCCCTGAATGCGGCT AAT-3\u0026rsquo;. PCR reaction conditions are set at 50\u0026deg;C for reverse transcription for 30 minutes, 95\u0026deg;C for denaturation for 15 minutes, followed by 35 cycles of 95\u0026deg;C for 30 seconds, 52\u0026deg;C for 45 seconds, 72\u0026deg;C for 90 seconds, and a final extension at 72\u0026deg;C for 5 minutes. The amplified products are validated using QIAxcel capillary electrophoresis before being sent to Shanghai Sangon Biotechnology Co., Ltd. for sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eARIMA model\u003c/h2\u003e \u003cp\u003eThe ARIMA model, as one of the common methods in time series analysis, reflects the development trend of time series data from the perspective of autocorrelation. It combines three components: autoregressive (AR), differencing (I), and moving average (MA), to capture trends and seasonal information in time series data. It can be used to forecast the incidence of respiratory infectious diseases. In this study, an ARIMA model was constructed based on weekly incidence data from June 2023 to February 2024, the Augmented Dickey-Fuller (ADF) test was performed using Eviews 12.0, using SPSS 27.0 for processing. The steps are as follows:(1). Data preprocessing: First, check for missing values in the data and replace them if necessary. Then, define the time and create a time series for the original data. (2). Stationarity identification: Identify the stationarity of the time series based on scatter plots, autocorrelation function, and partial autocorrelation function plots. Stationary non-stationary time series data until the autocorrelation function and partial autocorrelation function values are significantly non-zero. (3). Build ARIMA model: Establish the corresponding time series model based on the identified features, select the best-fitting model, and check if the residual sequence is a white noise sequence. (4). Use the validated model for forecasting.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eDemographic results\u003c/h2\u003e\n \u003cp\u003eSamples were collected weekly from June 2023 to February 2024, totaling 2046 samples. Among them, 1092 samples tested positive, resulting in an overall positive rate of 53.67%. The mean age was 36.8\u0026thinsp;\u0026plusmn;\u0026thinsp;20.6 years, with the highest number of individuals in the 14\u0026ndash;59 age group (733, 67.12%). There were 467 males (42.77%) and relatively more females (625, 57.23%). Among them, 125 individuals tested positive for mixed infections, accounting for 11.45%, while 968 individuals had single infections, accounting for 88.48%. The most common symptoms were cough 874 cases (79.60%), Sore throat 640 cases (58.29%), and Fatigue 516 cases (46.99%). (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic and clinical characteristics of positive cases\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClinical Symptoms\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.8\u0026thinsp;\u0026plusmn;\u0026thinsp;20.6 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e874(79.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeadache\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e479(43.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148(13.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSore throat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e640(58.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e733(67.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMuscle pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e346(31.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211(19.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNasal congestion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e388(35.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRunny nose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e504(45.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e467(42.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e516(46.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e625(57.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathogen detection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e967(88.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125(11.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e※P\u0026lt;0.05 shows that the difference is significant 503 cases (24.00%), influenza B virus 224 cases (10.95%), and HCoV 95 cases (4.64%)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eRespiratory virus infection rate\u003c/h2\u003e\n \u003cp\u003eThe most common pathogen was Flu A, with a positive rate of 24.00% (503 cases), all are H3N2 subtypes. This was followed by Flu B at 10.95% (224 cases), all of which were typed as B.Victoria. HCoV accounted for 4.64% (95 cases), including 33 cases of HCoV-229E, 50 cases of HCoV-OC43, 11 cases of HCoV-HKU1, and 1 case of HCoV-NL63. HRV accounted for 4.06% (83 cases), sequencing of rhinovirus with simple infection, including 18 cases of Rhinovirus A, 3 cases of Rhinovirus B, and 17 cases of Rhinovirus C. HMPV accounted for 3.81% (78 cases). HPIV accounted for 3.2% (65 cases), RSV for 2.25% (46 cases) (including 14 cases of RSVA ON1 and 32 cases of RSVB BA9), HADV for 2.0% (41 cases), HSV for 1.2% (29 cases), EV for 0.24% (5 cases) (including 1 case of CVA21, 3 cases of CVA6, and 1 case of D68). \u003cem\u003eM. Pneumonia\u003c/em\u003e accounted for 1.08% (22 cases), \u003cem\u003eH.influenzae\u003c/em\u003e for 0.7% (15 cases), S. pneumoniae for 0.34% (7 cases), HBoV for 0.1% (2 cases), and C. pneumoniae for 0.1% (2 cases). M.Pneumonia, and HADV infections are more common in children under 14 years old, while Flu A, Flu B, HCoV, and HRV infections mainly occur in the 14\u0026ndash;59 age group, with elderly individuals being more susceptible to HMPV infections. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetection of pathogens varies across different age groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePathogens\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;14 years (\u003cem\u003en\u003c/em\u003e,%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e14\u0026ndash;59\u0026nbsp;years\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e,%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;59\u0026nbsp;years\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e,%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHBoV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2(100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.578\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2(7.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18(66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(25.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.324\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS.pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5(71.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(28.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.977\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eC.pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2(100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.578\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eM.Pneumonia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e12(54.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10(45.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.693\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eH.influenzae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3(20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9(60.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.909\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHMPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8(10.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e33(45.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(43.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHADV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e25(60.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13(31.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(7.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlu A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e48(10.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e336(71.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85(18.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlu B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e38(19.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e138(70.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(10.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5(100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.167\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHPIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7(11.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e43(68.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(20.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCoV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4(4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e59(64.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(30.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHRV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8(9.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e55(66.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(24.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4(8.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e34(73.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(17.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e※P\u0026lt;0.05 shows that the difference is significant\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e* Denotes Fisher exact probability method\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eExcept for RSV, all other pathogens can cause symptoms such as cough, headache, sore throat, muscle pain, nasal congestion, runny nose, and fatigue. Among these, \u003cem\u003eH. influenzae\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(43, 21.83%), HMPV (59, 24.08%), HADV (38, 30.16%), Flu A (380, 22.77%), HPIV (23, 23.96%), and HCoV (165, 24.59%) infections commonly present with cough as the predominant symptom. HPIV (20, 20.83%) is the pathogen most likely to cause sore throat, while runny nose is most commonly associated with HADV infection (21, 16.67%). Please refer to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e for details.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical Symptom Distribution Characteristics of Different Pathogen Infections\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePathogens\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCough\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHeadache\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSore throat\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMuscle pain\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNasal congestion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRunny nose\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFatigue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHRV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49(20.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(12.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47(19.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(10.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(11.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(15.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(11.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.419*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(19.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(13.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(10.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(15.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS.pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.596*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eM.Pneumonia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(26.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(14.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(9.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(12.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(10.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHPIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(21.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(11.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(16.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(9.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(12.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(15.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(13.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCoV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59(24.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(13.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48(19.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(10.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(8.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(11.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(12.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(30.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(9.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(18.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(13.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(8.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlu A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e380(22.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214(12.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e280(16.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153(9.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163(9.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225(13.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254(15.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eH.influenzae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(19.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(12.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(19.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(11.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(9.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(11.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(16.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHMPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55(26.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(11.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(17.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(8.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(10.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31(14.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(10.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHADV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(23.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(15.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(20.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(11.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(7.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(8.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlu B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165(24.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87(12.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112(16.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60(8.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70(10.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85(12.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92(13.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHBoV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.856*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eC.pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003e※P\u0026lt;0.05 shows that the difference is significant\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003e* Denotes Fisher exact probability method\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe positive rate for mixed infections was 6.11% (125 out of 2046), with double infection accounting for 5.82% (119/2046), triple infection for 0.24% (5/2046), and quadruple infection for 0.05% (1/2046). The detection rate of Flu A\u0026thinsp;+\u0026thinsp;HRV is the highest in mixed infections at 20.69% (24 cases) followed by FluA\u0026thinsp;+\u0026thinsp;HCoV at 8.62%, (10 cases) and FluA\u0026thinsp;+\u0026thinsp;HSV at 11.11% (9 cases) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Flu A was mostly co-infected with other pathogens. Simultaneous or sequential infection of respiratory pathogens may lead to mixed infections, causing antagonistic or synergistic effects among pathogens and altering the severity of the disease. Comparative analysis was conducted between mixed infections and single infections based on Ct values, gender, age, and number of symptoms. (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In the cases of single infection, there were 585 females (58.15%) and 421 males (41.85%). For double-mixed infections, there were 49 males (41.17%) and 70 females (58.82%). In cases of multiple mixed infections, there were 2 males (33.33%) and 4 females (66.67%). The differences were not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).In different age groups, there was no statistical significance between simple infection and co-infection in the age group, and age did not affect co-infection. The pathogens in both simple and mixed infections most commonly caused cases to exhibit two symptoms, with 235 cases (24.38%) for simple infection and 34 cases (27.20%) for mixed infection. However, there was no statistically significant difference in the number of symptoms caused by the infection situation(p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). the ct value of simple infection HSV was larger, and the difference in Ct value was statistically significant.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of demographic characteristics and number of symptoms between mixed and single infection\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSingle infection(N,%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCo-infection(N,%)\u003c/p\u003e\n \u003cp\u003e2 \u0026gt;2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e421(41.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49(41.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.976*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e585(58.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70(58.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge group(yr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125(12.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(19.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.158*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e656(65.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71(59.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;59\u003c/p\u003e\n \u003cp\u003eNumber of symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186(18.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(21.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e216(22.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(18.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.496*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e235(24.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(27.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156(16.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(16.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110(11.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(11.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102(10.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(6.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(6.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(7.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80(8.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(10.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e※P\u0026lt;0.05 shows that the difference is significant\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e* Denotes Fisher exact probability method\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of Ct values between mixed and single infection\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 11.8778%;\"\u003e\n \u003cp\u003ePathogens\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 18.9076%;\"\u003e\n \u003cp\u003eSimple infection(N,%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 17.8167%;\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003cp\u003einfection(N,%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 15.1503%;\"\u003et\u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 14.9079%;\"\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 11.8778%;\"\u003e\n \u003cp\u003eHRV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9076%;\"\u003e\n \u003cp\u003e29.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.8167%;\"\u003e\n \u003cp\u003e28.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1503%;\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.9079%;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 11.8778%;\"\u003e\n \u003cp\u003eEV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9076%;\"\u003e\n \u003cp\u003e31.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.8167%;\"\u003e\n \u003cp\u003e32.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1503%;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.9079%;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 11.8778%;\"\u003e\n \u003cp\u003eHSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9076%;\"\u003e\n \u003cp\u003e34.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.8167%;\"\u003e\n \u003cp\u003e32.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1503%;\"\u003e\n \u003cp\u003e1.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.9079%;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 11.8778%;\"\u003e\n \u003cp\u003e\u003cem\u003eS. pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9076%;\"\u003e\n \u003cp\u003e34.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.8167%;\"\u003e\n \u003cp\u003e32.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1503%;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.9079%;\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 11.8778%;\"\u003e\n \u003cp\u003e\u003cem\u003eM. Pneumonia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9076%;\"\u003e\n \u003cp\u003e33.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.8167%;\"\u003e\n \u003cp\u003e27.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1503%;\"\u003e\n \u003cp\u003e4.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.9079%;\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 11.8778%;\"\u003e\n \u003cp\u003eHPIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9076%;\"\u003e\n \u003cp\u003e27.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.8167%;\"\u003e\n \u003cp\u003e27.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1503%;\"\u003e\n \u003cp\u003e2.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.9079%;\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 11.8778%;\"\u003e\n \u003cp\u003eHCoV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9076%;\"\u003e\n \u003cp\u003e29.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.8167%;\"\u003e\n \u003cp\u003e29.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1503%;\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.9079%;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 11.8778%;\"\u003e\n \u003cp\u003eRSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9076%;\"\u003e\n \u003cp\u003e31.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.8167%;\"\u003e\n \u003cp\u003e31.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1503%;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.9079%;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 11.8778%;\"\u003e\n \u003cp\u003eFlu A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9076%;\"\u003e\n \u003cp\u003e27.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.8167%;\"\u003e\n \u003cp\u003e27.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1503%;\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.9079%;\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 11.8778%;\"\u003e\n \u003cp\u003e\u003cem\u003eH. influenzae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9076%;\"\u003e\n \u003cp\u003e32.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.8167%;\"\u003e\n \u003cp\u003e32.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1503%;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.9079%;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 11.8778%;\"\u003e\n \u003cp\u003eHMPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9076%;\"\u003e\n \u003cp\u003e27.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.8167%;\"\u003e\n \u003cp\u003e29.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1503%;\"\u003e\n \u003cp\u003e1.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.9079%;\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 11.8778%;\"\u003e\n \u003cp\u003eHADV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9076%;\"\u003e\n \u003cp\u003e31.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.8167%;\"\u003e\n \u003cp\u003e32.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1503%;\"\u003e\n \u003cp\u003e1.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.9079%;\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 11.8778%;\"\u003e\n \u003cp\u003eFlu B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9076%;\"\u003e\n \u003cp\u003e27.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.8167%;\"\u003e\n \u003cp\u003e27.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 15.1503%;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.9079%;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 79.7511%;\"\u003e※P\u0026lt;0.05 shows that the difference is significant\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eInformation on mixed infections in this study. (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e): Difference in Ct values between mixed and simple infections.\u003c/p\u003e\n \u003cp\u003eThe study data was compared with respiratory pathogen surveillance data from Beijing\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e and Jinan\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e during the NPIS. In Beijing, the total positive detection rate was 10.97% during the NPIS period. The top five pathogen positives, from highest to lowest, were HCoV (2.42%), HRV (2.17%), HPIV (1.71%), Flu A and Flu B (1.50%), and RSV (1.23%). In Jinan, the overall positive detection rate was 40.18%. Among the top five pathogens, the positive rates were 9.85% for HRV, 8.94% for \u003cem\u003eM. Pneumonia\u003c/em\u003e, 6.53% for RSV, 3.13% for HPIV, and 2.16% for HADV.\u003c/p\u003e\n \u003cp\u003eIn this study, the positive detection rate began to increase in week 38 of 2023 (September 11\u0026ndash;17) and peaked in week 41 (October 2\u0026ndash;8), with the highest number of positive cases recorded in week 48 (November 20\u0026ndash;26) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). Influenza maintained a high level throughout the monitoring period. Flu A detection peaked in week 48 (November 20\u0026ndash;26) with a positive rate of 62.6% and remained high from week 41, 2023 to week 1, 2024 (October 2-January 7). Flu B showed a high rate from week 52, 2023 to week 6, 2024 (December 18,2023-February 11, 2024). HRV and HCoV detection peaked in week 39 (September 18\u0026ndash;24) and then gradually declined (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). EV, HPIV, HMPV, and RSV were detected throughout the monitoring period but with relatively low detection rates. HHADV had a relatively higher detection rate from week 52, 2023 to week 6, 2024, while \u003cem\u003eH. influenzae\u003c/em\u003e and S. \u003cem\u003epneumoniae\u003c/em\u003e were detected in weeks 49\u0026ndash;51 (November 27-December 17) and were not detected during other times.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eBuilding the ARIMA Model\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003eStabilizing the Series\u003c/h2\u003e\n \u003cp\u003eA model was established based on weekly incidence data from June 2023 to February 2024, and a time series plot was generated. The time series plot showed non-stationarity. Next, the stationarity of the series was confirmed using the Augmented Dickey-Fuller (ADF) unit root test in Eviews software, after conducting trend and intercept tests, with a reported t=-3.37, p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05. Thus, the original time series required stabilization. After applying a first-order difference (d\u0026thinsp;=\u0026thinsp;1), a transformed time series plot was generated. It was visually assessed for stationarity and tapering, indicating improved stationarity. The ADF unit root test yielded a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, confirming basic stationarity. However, seasonal differencing led to increased instability, so a first-order difference was chosen[Supplementary Fig.\u0026nbsp;1].\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eModel Identification\u003c/h2\u003e\n \u003cp\u003eACF and PACF plots were generated for the transformed series, showing zero-lag autocorrelation and partial autocorrelation after first-order differencing. ARIMA (0,1,4) (0,0,0) was selected as the optimal model after testing different p, d, and q values. The model had an R2 value of 0.930, and the standardized Bayesian Information Criterion (BIC) value was the smallest among all fitted models at 5.338. A residual test using Ljung-Box Q\u0026thinsp;=\u0026thinsp;10.930 and p\u0026thinsp;=\u0026thinsp;0.691 confirmed that the residual sequence was white noise. The mean absolute percentage error (MAPE) between actual and predicted values was 34.181, indicating a good model fit[Supplementary Fig.\u0026nbsp;2].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eModel Fitting\u003c/h2\u003e\n \u003cp\u003eBased on the weekly number of cases from June 2023 to February 2024, the ARIMA (0,1,4) (0,0,0) model was constructed. The optimal model was used to predict the number of respiratory pathogens until June 2024, as shown in the graph (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The actual number of cases, as seen from the table, all fall within the 95% confidence interval of the predicted values, indicating a good fit of the model.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eARIMA Model Fitting of positive detection rates for pathogens between Nov.2023 and Feb.2024\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eActual\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFitting\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026thinsp;~\u0026thinsp;26 November 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e132.99\u0026thinsp;~\u0026thinsp;170.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 November \u0026ndash; 3 December 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e43.00\u0026thinsp;~\u0026thinsp;80.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026ndash;10 December 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9.73\u0026thinsp;~\u0026thinsp;47.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u0026ndash;17 December 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9.23\u0026thinsp;~\u0026thinsp;46.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;24 December 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e28.76\u0026thinsp;~\u0026thinsp;66.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;31 December 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e28.65\u0026thinsp;~\u0026thinsp;66.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;7 January 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e31.63\u0026thinsp;~\u0026thinsp;69.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;14 January 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e27.67\u0026thinsp;~\u0026thinsp;65.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026ndash;21 January 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e76.28\u0026thinsp;~\u0026thinsp;113.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u0026ndash;28 January 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e36.93\u0026thinsp;~\u0026thinsp;74.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 January \u0026minus;\u0026thinsp;4 February 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e26.48\u0026thinsp;~\u0026thinsp;63.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLarge and medium-sized cities have a concentrated population, obvious seasonal climate, and high incidence of respiratory infectious diseases. This study focused on monitoring multiple respiratory pathogens in feverish populations in Nanjing communities to establish a respiratory pathogen spectrum and fill the gap in acute respiratory infectious disease monitoring after the cessation of NPIs. The study showed that from week 42 (October 2023), there was a rapid increase in positive pathogen samples, maintaining a stable and high positive detection rate, dropping in December, and then showing a growth trend until early February 2024. This study conducted molecular epidemiological research on respiratory pathogens to identify the currently dominant genotypes, which contributes to a deeper understanding of the epidemiological characteristics of different pathogens, including their transmission routes, infectivity, and seasonal distribution. The overall positive detection rate in this study was 53.57%, with high detection rates for Flu A and Flu B (24.00% and 10.95%, respectively). All Flu A subtypes were H3N2, while all Flu B subtypes were B/Victoria lineage. Influenza viruses showed a peak in winter and spring, with Flu A prevalent from October to January and Flu B prevalent from December to February, consistent with research in Beijing\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. However, the high detection rate of influenza viruses in our study indicates a serious influenza situation in our city.\u003c/p\u003e \u003cp\u003eHRV was prevalent from August to October, consistent with research in Taizhou\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. This study conducted typing tests on simple infection of rhinoviruses and enteroviruses, detecting a total of 18 strains of rhinovirus A, including 3 of type A21, 2 of type A64, 2 of type A7, 1 of type A71, and 3 of type A98, as well as 3 strains of rhinovirus B, including 1 of type B69, and 17 strains of rhinovirus C, including 2 of type C1. It can be observed that the detection rates of HRV A and C types are relatively high, consistent with domestic and international research results\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. HRV-A and HRV-C are more likely to cause moderate to severe diseases, and type C is associated with childhood asthma attacks. Therefore, strengthening the monitoring of rhinovirus typing is of great significance for the prevention and control of respiratory infectious diseases. In enteroviruses, a total of 3 strains of coxsackievirus A6, 1 strain of coxsackievirus A21, and 1 strain of enterovirus D68 were detected. Coxsackievirus A6 has become one of the main pathogens causing human hand-foot-and-mouth disease in recent years. While coxsackievirus A21 and enterovirus D68 can also enter the body through the respiratory tract, they rarely cause hand-foot-and-mouth disease and herpangina, mainly manifesting as symptoms of upper respiratory tract infections. HPIV, HCoV, HSV, EV, and HADV showed short-term low prevalence. Among them, HPIV is mainly type HPIV-3, and among the four serotypes, the infection rate of HPIV-3 is the highest, often peaking in the winter and spring seasons, consistent with previous research findings \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. HCoV mainly invaded the upper respiratory tract with HCoV-OC43 and HCoV-229E types. HMPV, \u003cem\u003eH. influenzae\u003c/em\u003e, \u003cem\u003eS. pneumoniae\u003c/em\u003e, and \u003cem\u003eC. pneumoniae\u003c/em\u003e had relatively low annual detection rates, appearing sporadically. However, due to the short duration of our study and the lack of coverage in the spring, it is not sufficient to fully describe the common respiratory pathogen prevalence characteristics in the region, requiring further supplementation in the future.\u003c/p\u003e \u003cp\u003eMixed infections of pathogens may pose challenges to the diagnosis, treatment, and epidemic prevention and control of respiratory infections. Concurrent or sequential infection of respiratory pathogens may lead to mixed infections, causing positive synergistic or negative antagonistic interactions among pathogens, leading to varying degrees of disease severity changes in patients. In this study, mixed infections accounted for 11.45% of total positive cases, with Flu A mostly co-infected with other pathogens, and the highest positive detection rates in mixed infections were observed for Flu A\u0026thinsp;+\u0026thinsp;HRV, FluA\u0026thinsp;+\u0026thinsp;HCoV, FluA\u0026thinsp;+\u0026thinsp;HSV. Previous studies suggested negative interactions between IAV and RSV, HRV and IAV, while RSV and HRV co-infections indicated increased disease severity\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Previous studies have shown that \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e,co-infections may lead to an increased hospitalization rate among patients with respiratory viral infections, indicating an escalation in disease severity. This study conducted a comparative analysis of single infections and co-infections based on gender, different age groups, Ct values, and the number of symptoms. The results ultimately revealed statistically significant differences between single infections and co-infections across different age groups. The lack of statistical significance in symptom numbers may be due to the challenge of deriving conclusions about severity solely based on symptom counts. HRV, HSV, \u003cem\u003eM. Pneumoniae\u003c/em\u003e, and \u003cem\u003eS. pneumoniae\u003c/em\u003e in mixed infections had smaller Ct values compared to single infections, possibly due to synergistic effects between pathogens, resulting in increased disease severity. The Ct value for HSV single infection was 34.57, while for mixed infection, it was 32.55, with significant differences and higher persuasiveness. However, HADV and HMPV single infection had larger Ct values, possibly related to their role as primary infecting viruses activating the host's non-specific innate immune response. Due to the short study period and relatively low number of mixed infection cases, significant results could not be obtained. Viral interference may provide a new model for antiviral treatment research. Some studies have shown that Influenza A virus Defective Interfering Particles (IAV-DIPs) can stimulate the host's innate immune system to inhibit HSV infection and replication\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, suggesting a potential preventive and therapeutic role in respiratory infectious diseases. There is limited research in China in this area, and future monitoring of more data can lead to further research. Subsequent follow-up tracking or research based on hospital cases could further investigate this matter.\u003c/p\u003e \u003cp\u003eStarting from January 2023, China lifted the control measures for COVID-19 from Class A infectious diseases. This study was conducted from June 2023 to the end of February 2024, after comprehensive relaxation of epidemic control measures. The aim was to explore the changes in the respiratory pathogen spectrum after the cessation of NPIs. Since the emergence of COVID-19, China has implemented non-pharmaceutical interventions (NPIs) including encouraging mask-wearing, patient isolation, social distancing, hand hygiene, and disinfection to prevent new SARS-CoV-2 infections. Comparing the spectrum of respiratory pathogens in Jinan City and Beijing City during NPIS period with that in this study, the overall positive detection rate of pathogens in this study (54.15%) was significantly higher than that in Jinan City (40.18%) and Beijing (10.97%), indicating that NPIS measures against COVID-19 greatly reduced the prevalence of respiratory pathogens. Furthermore, the detection rate of influenza in this study was 34.95% ((24.00% + 0.95%), which was significantly higher than 3.44% in Jinan City and 1.5% in Beijing City, and the positive detection rate of all pathogens in this study was higher than that in Beijing city, possibly because NPIS measures during COVID-19 not only prevented the invasion of viruses but also cut off the transmission of other respiratory pathogens. However, the overall positive rate of respiratory pathogens is rising, which may be linked to the public's relaxation of vigilance against respiratory infectious diseases, and may also be related to the immune debt after the novel coronavirus pandemic, resulting in a rebound or high epidemic level of some infectious diseases. However, during NPIS, the positive rate of HRV pathogens in Jinan City was 9.85%, higher than 8.7% in this study, which may be since HRV is transmitted through direct or indirect contact with contaminated items, which requires chlorine-based disinfectants to eradicate, and the use of ethanol is less effective. In addition, the positive rates of Mycobacterium pneumoniae and respiratory syncytial virus in Jinan were significantly higher than in our study, which may be due to the fact that our study focussed on community populations rather than hospital-based studies, and that Jinan has a higher proportion of children under the age of 15, who are more susceptible to these pathogens.\u003c/p\u003e \u003cp\u003eIn recent years, there has been extensive research in China utilizing the ARIMA model for infectious disease surveillance and prediction, demonstrating its effectiveness, particularly in short-term forecasting\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Based on the scientifically predicted results of the model, early detection of respiratory pathogen trends can be achieved, providing timely warnings for control efforts and facilitating the targeted formulation of prevention and control strategies. In this study, fitting models were established using the ARIMA model (0,1,4), (0,0,0) based on influenza surveillance data from June 2023 to February 2024. According to the forecast results of the ARIMA model, influenza peaks are expected to occur in late autumn and winter of 2023, with the number of detected respiratory pathogens projected to decline initially from March to June 2024 before stabilizing. This trend may be attributed to the rising temperatures during the spring and summer seasons. Nanjing, characterized by a subtropical monsoon climate, experiences a noticeable temperature increase by the end of February along with high humidity and rainfall. Studies have indicated that the transmission of respiratory viruses is associated with climate conditions, especially humidity and temperature, with respiratory pathogens being more likely to spread under cold and dry conditions\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Additionally, this study has certain limitations as it only considers the quantity of detected pathogens, potentially leading to underreporting or overreporting biases in weekly data.\u003c/p\u003e \u003cp\u003eOverall, our study monitored respiratory infections in the community population of Nanjing City, providing insights into the spectrum and co-infections of respiratory pathogens., A time series forecasting model has been established to serve as a reference for prevention and control efforts. While filling gaps in Nanjing's respiratory pathogen spectrum research, our study has limitations due to a short period and single sample source. Future research could involve hospital samples to further understand the epidemiology of respiratory pathogens, establish a more comprehensive pathogen spectrum, and enhance Nanjing's monitoring and alert system post-COVID-19 pandemic.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: Our study did not require further ethics committee approval as it did not involve animal or human clinical trials and was not unethical. By the ethical principles outlined in the Declaration of Helsinki, all participants provided informed consent before participating in the study. The anonymity and confidentiality of the participants were guaranteed, and participation was completely voluntary.\u003c/p\u003e\n\u003cp\u003eConsent for publication: All authors approved the final manuscript and the submission to this journal.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests: No conflicts of interest.\u003c/p\u003e\n\u003cp\u003eFunding: This work was supported by National Natural Science Foundation of China (82222062); Jiangsu Province 333 project ; Scientific research project of Jiangsu health commission (DX202301); Social Development Foundation of Jiangsu Province \u0026nbsp; (BE2021739); Science and Technology Project of Jiangsu Province (BE2023601) .\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions: Fei Deng was responsible for completing most of the experimental work and contributed to the data collation. Zhuhan Dong did all the data collection and analysis, and she wrote the first draft of the manuscript. Tian Qiu, Ke Xu, Qigang Dai, Huiyan Yu, Huan Fan, Haifeng Qian, and Changjun Bao participated in the analysis and interpretation of all data. Liguo Zhu designed the project, planned the experiments, verified the data collection, analysis and interpretation, and revised the original manuscript into the final version submitted. All authors reviewed the manuscript, agreed to take responsibility for all aspects of the work, and testified to the accuracy and completeness of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2017 Influenza Collaborators. Mortality, morbidity, and hospitalizations due to influenza lower respiratory tract infections, 2017: an analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2019;7(1):69-89. \u003c/li\u003e\n\u003cli\u003eYang J, McClymont H, Wang L, Vardoulakis S, Hu W. 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Viruses. 2023;15(9):1872. \u003c/li\u003e\n\u003cli\u003eTang lin,Lv wenli, Bao lili, el al. Predictive Analysis of Influenza-like Cases in Chifeng Based on ARIMA Model Predictive Analysis. Chinese Journal of Social Medicine. 2023,40(03):350-354.\u003c/li\u003e\n\u003cli\u003eZou Xiaojiang, Zhao Han, Wang Qiyin, Ye Mengliang. Prediction of influenza in Chongqing, China, based on the Autoregressive Integrated Moving Average model. Journal of Chongqing Medical University. 2023,48(12):1425-1429.\u003c/li\u003e\n\u003cli\u003eSloan C, Moore ML, Hartert T. Impact of pollution, climate, and sociodemographic factors on spatiotemporal dynamics of seasonal respiratory viruses. Clin Transl Sci. 2011;4(1):48-54.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"virology-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"virj","sideBox":"Learn more about [Virology Journal](http://virologyj.biomedcentral.com/)","snPcode":"12985","submissionUrl":"https://submission.nature.com/new-submission/12985/3","title":"Virology Journal","twitterHandle":"@VirologyJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute respiratory tract infection, Pathogen spectrum, Arima model, Virus, Bacteria","lastPublishedDoi":"10.21203/rs.3.rs-4645900/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4645900/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRespiratory infectious disease was the world's highest incidence of infectious diseases, it was caused by a variety of respiratory pathogens, and the current monitoring of respiratory pathogens in the world focused on influenza and coronavirus. This study aimed to establish the pathogen spectrum of local acute respiratory infections and to further study the co-infection of pathogens. Time series models commonly used to predict infectious diseases can effectively predict disease outbreaks and serve as auxiliary tools for disease surveillance and response strategy formulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom June 2023 to February 2024, we collected influenza-like illness (ILI) cases weekly from the community in Xuanwu District, Nanjing, and obtained a total of 2,046 samples. We established a spectrum of respiratory pathogens in Nanjing and analyzed the age distribution and symptom counts associated with various pathogens. We compared age, gender, symptom counts, and viral loads between individuals with co-infections and those with single infections. An autoregressive comprehensive moving average model (ARIMA) was constructed to predict the incidence of respiratory infectious diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 2046 samples, the total detection rate of respiratory pathogen nucleic acids was 53.57% (1096/2046), with influenza A virus 503 cases (24.00%), influenza B virus 224 cases (10.95%), and HCoV 95 cases (4.64%) being predominant. Some pathogens were statistically significant in age and number of symptoms. The positive rate of mixed infections was 6.11% (125/2046), There was no significant difference in age and number of symptoms between co-infection and simple infection. After multiple iterative analyses, an ARIMA model (0,1,4), (0,0,0) was established as the optimal model, with an R\u003csup\u003e2\u003c/sup\u003e value of 0.930, indicating good predictive performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the past, the spectrum of respiratory pathogens in Nanjing, Jiangsu Province was complex, and the main age groups of different viruses were different, causing different symptoms, and the co-infection of viruses had no correlation with the age and gender of patients. The ARIMA model provided an estimate of future incidence, which plateaued in subsequent months.\u003c/p\u003e","manuscriptTitle":"Respiratory Pathogen Dynamics in Community Fever Cases — Jiangsu Province, China (2023-2024)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 22:04:54","doi":"10.21203/rs.3.rs-4645900/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2024-08-15T12:25:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-09T17:51:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335899119010464732857563123003503755215","date":"2024-08-05T12:16:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-04T16:00:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29291026628087269080407953611718404515","date":"2024-07-31T12:11:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102527556869420484726065597437604411","date":"2024-07-31T04:55:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274760006683761726407520537501414399624","date":"2024-07-31T03:31:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-31T00:42:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-01T06:21:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-01T06:16:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Virology Journal","date":"2024-06-27T04:11:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"virology-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"virj","sideBox":"Learn more about [Virology Journal](http://virologyj.biomedcentral.com/)","snPcode":"12985","submissionUrl":"https://submission.nature.com/new-submission/12985/3","title":"Virology Journal","twitterHandle":"@VirologyJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ca05c57a-ba76-45d1-aacd-a63682b7ed73","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-27T10:49:06+00:00","versionOfRecord":{"articleIdentity":"rs-4645900","link":"https://doi.org/10.1186/s12985-024-02494-9","journal":{"identity":"virology-journal","isVorOnly":false,"title":"Virology Journal"},"publishedOn":"2024-09-20 15:57:44","publishedOnDateReadable":"September 20th, 2024"},"versionCreatedAt":"2024-07-23 22:04:54","video":"","vorDoi":"10.1186/s12985-024-02494-9","vorDoiUrl":"https://doi.org/10.1186/s12985-024-02494-9","workflowStages":[]},"version":"v1","identity":"rs-4645900","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4645900","identity":"rs-4645900","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}